Building an open-source simulation platform of acoustic radiation force-based breast elastography

Characterizing Mechanical Properties of Soft Tissues Using Non-contact Displacement Measurements: How Should We Assess the Uncertainty?

Techniques aimed at the non-invasive characterization of soft tissues according to elastic properties are rapidly evolving. Virtual touch-based elastographic methods including acoustic radiation force imaging (ARFI) and optical elastography measure the peak axial displacement (PD) and time-to-peak-displacement (TTP) of tissue in response to a localized force. These measurements have been used clinically to differentiate tissues, albeit with mixed results. However, to date, the reason has not been fully understood. In this study, we apply a novel modeling approach to explore the mechanistic link between simplistic displacement measurements and tissue viscoelasticity in the application of virtual touch-based elastographic methods to staging chronic liver disease (CLD). To our knowledge, such a study has not been reported in the literature. Specifically, a numerical screening study was first conducted to identify factors that most strongly determine PD and TTP. Response surface experimental designs were then applied to these factors to produce meta-models of expected PD and TTP probability density functions (PDFs) as functions of identified factors. Results from the screening study suggest that both PD and TTP measurements are primarily influenced by three factors: the initial Young's modulus of the tissue, the first viscoelastic Prony series time constant, and pre-compression applied during acquisition. To investigate the implications of these results, stochastic inputs for these three factors associated were used to determine a robust response surface. The identified response surface methodology can be used to determine optimal cutoff values for PD and TTP that could be used in order to stage chronic liver disease.

An Improved Region-Growing Motion Tracking Method Using More Prior Information for 3-D Ultrasound Elastography

Three-dimensional (3-D) ultrasound elastography can provide 3-D tissue stiffness information that may be used during clinical diagnoses. In the framework of strain elastography, motion tracking plays an important role. In this study, an improved 3-D region-growing motion tracking (RGMT) algorithm based on a concept of exterior boundary points was developed. In principle, the proposed method first determines displacement at some seed points by strictly checking the local correlation and continuity in the neighborhood of those seeds. Subsequent displacement estimation is then conducted from these initial seeds to obtain displacements associated with other locations. This RGMT algorithm is designed to use more known information-including displacements and correlation values of all known-displacement neighboring points-to estimate the displacement of an unknown-displacement point, whereas previous RGMT methods employed information from only one such point. The algorithm was tested on 3-D ultrasound volumetric data acquired from a simulation, a tissue-mimicking phantom, and five human subjects. Motion-compensated cross correlations (MCCCs), strain contrast, and displacement Laplacian values (representing smoothness of an estimated displacement field) were calculated and used to evaluate the merits of the proposed RGMT method. Compared with a previously published RGMT method, the results show that the proposed RGMT method can provide smaller displacement errors and smoother displacements and improve strain contrast while maintaining reasonably high MCCC values, indicating good motion tracking quality. The proposed method is also computationally more efficient. In summary, our preliminary results demonstrated that the proposed RGMT algorithm is capable of obtaining high-quality 3-D strain elastographic data using modified clinical equipment.

Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter referred to as CNN-based tracking) by the computer vision community for breast ultrasound strain elastography. Elastographic datasets produced by finite element and ultrasound simulations were used to retrain three published CNN models: FlowNet-CSS, PWC-Net, and LiteFlowNet. After retraining, the three improved CNN models were evaluated using computer-simulated and tissue-mimicking phantoms, and in vivo breast ultrasound data. CNN-based tracking results were compared with two published two-dimensional (2D) speckle tracking methods: coupled tracking and GLobal Ultrasound Elastography (GLUE) methods. Our preliminary data showed that, based on the Wilcoxon rank-sum tests, the improvements due to retraining were statistically significant (p < 0.05) for all three CNN models. We also found that the PWC-Net model was the best neural network model for data investigated, and its overall performance was on par with the coupled tracking method. CNR values estimated from in vivo axial and lateral strain elastograms showed that the GLUE algorithm outperformed both the retrained PWC-Net model and the coupled tracking method, though the GLUE algorithm exhibited some biases. The PWC-Net model was also able to achieve approximately 45 frames/second for 2D speckle tracking data investigated.

Modeling Uncertainty of Strain Ratio Measurements in Ultrasound Breast Strain Elastography: A Factorial Experiment

Strain elastography (SE) is a technique in which images of localized tissue strains are used to detect the relative stiffness of tissues. The application of SE in differentiating malignant breast lesions from benign ones has been broadly investigated. The strain ratio (SR) between the background and the breast tumor has been used and its results have been mixed. Due to the complex nature of tissue elasticity and how it relates to the strain fields measured in SE, the exact reason is not known. In this study, we apply a novel design-of-experiments-based metamodeling approach to mechanical simulation of SE in the human breast. To our knowledge, such a study has not been reported in the ultrasound SE literature. More specifically, we first conduct a screening study to identify the biomechanical factors/simulation inputs that most strongly determine SR. We then apply a response surface experimental design to these factors to produce a metamodel of SR as a function of said factors. Results from the screening study suggest that the SR measurements are primarily influenced by three factors: the initial shear modulus of the lesion, the elastic nonlinearity of the lesion, and the precompression applied during acquisition. In order to investigate the implications of these results, stochastic inputs for these three factors associated with the malignant and benign cases were applied to the resulting response surface. The resulting optimal cutoffs, sensitivity, and specificity were generally in line with a majority (>60%) of 19 clinical trials in the literature.

A REAL-TIME MEDICAL ULTRASOUND SIMULATOR BASED ON A GENERATIVE ADVERSARIAL NETWORK MODEL

This paper presents an artificial intelligence-based ultrasound simulator suitable for medical simulation and clinical training. Particularly, we propose a machine learning approach to realistically simulate ultrasound images based on generative adversarial networks (GANs). Using B-mode ultrasound images simulated by a known ultrasound simulator, Field II, an "image-to-image" ultrasound simulator was trained. Then, through evaluations, we found that the GAN-based simulator can generate B-mode images following Rayleigh scattering. Our preliminary study demonstrated that ultrasound B-mode images from anatomies inferred from magnetic resonance imaging (MRI) data were feasible. While some image blurring was observed, ultrasound B- mode images obtained were both visually and quantitatively comparable to those obtained using the Field II simulator. It is also important to note that the GAN-based ultrasound simulator was computationally efficient and could achieve a frame rate of 15 frames/second using a regular laptop computer. In the future, the proposed GAN-based simulator will be used to synthesize more realistic looking ultrasound images with artifacts such as shadowing.

Analytical Minimization-Based Regularized Subpixel Shear-Wave Tracking for Ultrasound Elastography

Ultrasound elastography is a convenient and affordable method for imaging mechanical properties of tissue, which are often correlated with pathologies. An emerging novel elastography technique applies an external acoustic radiation force to generate a shear wave in the tissue and uses ultrasound imaging to track the shear wave. Accurate tracking of the small tissue motion is a critical step in shear-wave elastography (SWE), but it is challenging due to various sources of noise in the ultrasound data. We formulate tissue displacement estimation as an optimization problem and propose two computationally efficient approaches to estimate the displacement field. The first algorithm is referred to as dynamic programming analytic minimization (DPAM), which utilizes first-order Taylor series expansion of the highly nonlinear cost function to allow for its efficient optimization, and was previously proposed for quasistatic elastography. The second algorithm is a novel technique that utilizes second-order derivatives of the nonlinear cost function. We call the new algorithm second-order analytic minimization elastography (SESAME). We compare DPAM and SESAME to the standard normalized cross correlation (NCC) approach in the context of displacement and speed estimation of wave propagation in SWE. The results of micrometer-order displacement estimation in a uniform simulation phantom illustrate that SESAME outperforms DPAM, which in turn outperforms NCC in terms of signal-to-noise ratio (SNR) and jitter. In addition, the relative difference between true and reconstructed shear modulus (averaged over excitations at different focal depths and several scatterer realizations at each depth) is approximately 3.41%, 1.12%, and 1.01%, respectively, for NCC, DPAM, and SESAME. The performance of the proposed methods is also assessed with real data acquired using a tissue-mimicking phantom, wherein, in comparison to NCC, DPAM and SESAME improve the SNR of displacement estimates by 7.6 and 9.5 dB, respectively. Experimental results on a tissue-mimicking phantom also show that shear modulus reconstruction substantially improved with the proposed DPAM technique over NCC and with some further improvement achieved by utilizing the second-order Taylor series approximation in SESAME instead of the first-order DPAM.

A two-dimensional (2D) systems biology-based discrete liver tissue model: A simulation study with implications for ultrasound elastography of liver fibrosis

Continuum tissue models that were often used to simulate or analyze the mechanical properties of tissues being imaged may not be biologically realistic. Our primary objective was to establish the feasibility of using systems biology to construct biologically relevant tissue models linking tissue structure, composition and architecture to the ultrasound measurements directly. The first application was designated to model fibrotic liver tissues. The proposed liver tissue model leveraged established histopathology knowledge of fibrotic liver tissues. Particularly, rules of systems biology derived from molecular histopathology were first implemented into an agent-based software platform SPARK to reflect progressions of liver fibrosis with/without steatosis. Then, microscopic compositions of tissues (e.g. cellular components) were converted to computing grids (at the 50-100 μm scale) for wave simulations using an open-source K-Wave. To verify the physical soundness of the proposed model, virtual wave speed measurements (i.e. shear wave speed [SWS] and the speed of sound [SOS]) were performed. Our initial results demonstrated that the simulated SWS values increased with the progression of liver fibrosis (from 1.5 m/s [Fibrosis stage 1] to 4 m/s [Fibrosis stage 4]). Similarly, the simulated SOS values were within the range of clinical data (from 1575 m/s [Fibrosis stage 0-3] to 1594 m/s [Fibrosis stage 4]). In summary, we found that those systems biology simulated fibrotic liver tissues with and without steatosis can reflect spatial characteristics of relevant histology. Also, their mechanical characteristics (i.e. shear/compressional wave speed) were in good agreement with data reported in the clinical literature.

Fourier-Domain Shift Matching: A Robust Time-of-Flight Approach for Shear Wave Speed Estimation

Our primary objective of this work was to design and test a new time-of-flight (TOF) method that allows measurements of shear wave speed (SWS) following impulsive excitation in soft tissues. Particularly, under the assumption of the local plane shear wave, this work named the Fourier-domain shift matching (FDSM) method, estimates SWS by aligning a series of shear waveforms either temporally or spatially using a solution space deduced by characteristic curves of the well-known 1-D wave equation. The proposed SWS estimation method was tested using computer-simulated data, and tissue-mimicking phantom and ex vivo tissue experiments. Its performance was then compared with three other known TOF methods: lateral time-to-peak (TTP) method with robust random sampling consensus (RANSAC) fitting method, Radon sum transformation method, and a modified cross correlation method. Hereafter, these three TOF methods are referred to as the TTP-RANSAC, Radon sum, and X-corr methods, respectively. In addition to an adapted form of the 2-D Fourier transform (2-D FT)-based method in which the (group) SWS was approximated by averaging phase SWS values was considered for comparison. Based on data evaluated, we found that the overall performance of the above-mentioned temporal implementation of the proposed FDSM method was most similar to the established Radon sum method (correlation = 0.99, scale factor = 1.03, and mean difference = 0.07 m/s), and the 2-D FT (correlation = 0.98, scale factor = 1.00, and mean difference = 0.10 m/s) at high signal quality. However, results obtained from the 2-D FT method diverged (correlation = 0.201) from these of the proposed temporal implementation in the presence of diminished signal quality, whereas the agreement between the Radon sum approach and the proposed temporal implementation largely remained the same (correlation = 0.98).

Influence of Tissue Microstructure on Shear Wave Speed Measurements in Plane Shear Wave Elastography: A Computational Study in Lossless Fibrotic Liver Media

Shear wave elastography (SWE) has been used to measure viscoelastic properties for characterization of fibrotic livers. In this technique, external mechanical vibrations or acoustic radiation forces are first transmitted to the tissue being imaged to induce shear waves. Ultrasonically measured displacement/velocity is then utilized to obtain elastographic measurements related to shear wave propagation. Using an open-source wave simulator, k-Wave, we conducted a case study of the relationship between plane shear wave measurements and the microstructure of fibrotic liver tissues. Particularly, three different virtual tissue models (i.e., a histology-based model, a statistics-based model, and a simple inclusion model) were used to represent underlying microstructures of fibrotic liver tissues. We found underlying microstructures affected the estimated mean group shear wave speed (SWS) under the plane shear wave assumption by as much as 56%. Also, the elastic shear wave scattering resulted in frequency-dependent attenuation coefficients and introduced changes in the estimated group SWS. Similarly, the slope of group SWS changes with respect to the excitation frequency differed as much as 78% among three models investigated. This new finding may motivate further studies examining how elastic scattering may contribute to frequency-dependent shear wave dispersion and attenuation in biological tissues.